Machine Learning-Enhanced Engineering of Ultrahigh-Loading Polymer-Drug Assemblies with Programmable ROS-Triggered Release for Tumor Elimination

Machine Learning-Enhanced Engineering of Ultrahigh-Loading Polymer-Drug Assemblies with Programmable ROS-Triggered Release for Tumor Elimination | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Machine Learning-Enhanced Engineering of Ultrahigh-Loading Polymer-Drug Assemblies with Programmable ROS-Triggered Release for Tumor Elimination Craig Duvall, Richard d'Arcy, Sarah Hall, Steve Wilson, Mariah Bezold, and 15 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8507915/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Overcoming low solubility and bioavailability challenges is critical for enhancing therapeutic efficacy and reducing systemic toxicity of active pharmaceutical ingredients. Here, we describe the rational design of polysulfide nanocarriers that leverage optimized non-covalent polymer-drug interactions—specifically hydrogen bonding, π-interactions, and ion-pairing—to achieve ultrahigh loading capacities (up to ~50 wt.%) across diverse small-molecule therapeutics. Employing machine learning and Bayesian-driven feature refinement, only 4 critical features accurately predicted loading capacity with a mean absolute error of only 2.23% for physically loaded drugs. By systematically tuning polysulfide composition, we created ROS-responsive copolymers capable of stable drug encapsulation and controlled, oxidation-triggered release, tailored for cancer environments. As proof-of-concept, ultrahigh-loaded paclitaxel-polysulfide particles were formulated, showing significantly increased maximum tolerated doses, plasma circulation, tumor retention, and antitumor efficacy compared to clinical standard Taxol. Notably, a Slow-release formulation outperformed a Fast-release formulation, achieving complete tumor eradication in 3-out-of-8 animals in an orthotopic triple negative breast cancer model, emphasizing the importance of curated drug-polymer interactions in nanomedicine. This approach provides critical insights for advancing next-generation delivery platforms and broadening clinical translation possibilities. Physical sciences/Nanoscience and technology/Nanomedicine/Drug delivery Physical sciences/Materials science/Nanoscale materials/Nanoparticles Physical sciences/Chemistry/Polymer chemistry/Polymer synthesis Biological sciences/Biotechnology/Biomaterials/Drug delivery Full Text Additional Declarations There is NO Competing Interest. Supplementary Files SupplementaryDataSet.xlsx Supplementary Data Set SupportingInformation.docx Supplementary Information Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8507915","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":584425017,"identity":"e0cc19b7-3412-4eca-95fd-b33882e80d99","order_by":0,"name":"Craig 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